Abstract
Most real-world optimization problems tackle a large number of decision variables, known as Large-Scale Global Optimization (LSGO) problems. In general, the metaheuristic algorithms for solving such problems often suffer from the “curse of dimensionality.” In order to improve the disadvantage of Grey Wolf Optimizer when solving the LSGO problems, three genetic operators are embedded into the standard GWO and a Hybrid Genetic Grey Wolf Algorithm (HGGWA) is proposed. Firstly, the whole population using Opposition-Based Learning strategy is initialized. Secondly, the selection operation is performed by combining elite reservation strategy. Then, the whole population is divided into several subpopulations for cross-operation based on dimensionality reduction and population partition in order to increase the diversity of the population. Finally, the elite individuals in the population are mutated to prevent the algorithm from falling into local optimum. The performance of HGGWA is verified by ten benchmark functions, and the optimization results are compared with WOA, SSA, and ALO. On CEC’2008 LSGO problems, the performance of HGGWA is compared against several state-of-the-art algorithms, CCPSO2, DEwSAcc, MLCC, and EPUS-PSO. Simulation results show that the HGGWA has been greatly improved in convergence accuracy, which proves the effectiveness of HGGWA in solving LSGO problems.
Highlights
Large-Scale Global Optimization (LSGO) is widely applied in practical engineering problems, such as large-scale job shop scheduling problem [1], large-scale vehicle routing problems [2], and reactive power optimization of large-scale power system [3]
The 10 highdimensional benchmark functions in Table 2 are optimized by Hybrid Genetic Grey Wolf Algorithm (HGGWA) algorithm proposed in this paper, and the results are compared with Whale Optimization Algorithm (WOA), Salp Swarm Algorithm (SSA), and ALO
We focus on investigating the optimization performance of the proposed method on problems with D = 100, 500, and 1000
Summary
Large-Scale Global Optimization (LSGO) is widely applied in practical engineering problems, such as large-scale job shop scheduling problem [1], large-scale vehicle routing problems [2], and reactive power optimization of large-scale power system [3]. In the famous book A Discourse on Method [7], Descartes pointed out that it is necessary to study complex problems and decompose them into a number of relatively simple small problems and solve them one by one He called it a “divide and conquer” strategy, which is to solve the whole problem by decomposing the original large-scale problem into a set of smaller and simpler subproblems which are more manageable and easier to solve and solve each subproblem independently. To direct at the large-scale global optimization problems, this paper improves the HGGWA algorithm proposed in literature [17] by adding a parameter nonlinear adjustment strategy, which further improves the global convergence and solution accuracy.
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